Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The EU Cyber Resilience Act (CRA) is a sweeping regulation that mandates security by design and uniform cybersecurity standards for all hardware and software products with digital elements sold in the European Union. The CRA officially entered into force in December 2024, with compliance enforcement phased in over the coming years.

AI didn’t just change how fast you ship. It changed what your AI application security program has to protect. Two years ago, security teams protected code, open source, and containers. Today they also have to protect AI agents, MCP servers, models, prompts, and runtime interactions, configured or deployed faster than any team can manually review. The attack surface didn’t grow. It exploded.

TL;DR: AI security testing solutions find vulnerabilities in code, dependencies, and AI components across the dev pipeline.

Mend.io’s research team caught this campaign before most of the open source community ever saw it. Continuous monitoring of RubyGems flagged a batch of gems that looked, at a glance, like an ordinary cryptomining squat, and Mend.io reported the full batch to RubyGems for takedown. Every gem was pulled within hours. Mend.io’s team also pulled two of the samples apart in full, because knowing a campaign exists isn’t the same as knowing how it works.

AI Security Posture Management (AI-SPM) platforms are specialized tools that discover, monitor, and secure AI models, pipelines, and data, mitigating risks like data leakage and model poisoning. They offer continuous visibility, manage misconfigurations, and enforce security policies across cloud services like Azure OpenAI and Bedrock.

Why the independent layer keeps winning as the models get better, not despite them. This series has been building to one question, and it is the objection every honest reader has been holding since the first piece. If the frontier models keep getting better this fast, why does an independent security layer keep winning? Why not wait for the model that writes safe code and verifies its own work?

Dependency management tools are software solutions designed to automate and streamline the process of handling external libraries, modules, or packages that a project relies on. These tools help developers specify, install, update, and track dependencies, ensuring that all required components are present and compatible.

On July 7, 2026, the European Central Bank sent a letter to the CEO of every bank it directly supervises with an unambiguous instruction: build a formal action plan against AI-enabled cyberattacks, and submit it to your supervisory team by October 31.

AI governance platforms provide enterprises with centralized oversight to manage AI risks, ensure regulatory compliance, and automate policy enforcement across the AI lifecycle. Leading solutions include security-oriented tools like Mend.io, HiddenLayer, and Prompt Security, as well as end-to-end governance platforms like IBM watsonx.governance and Microsoft Purview.

TL;DR: AI governance solutions help organizations inventory, secure, and monitor AI systems. Best for AI security and shadow AI: Mend AI; enterprise risk and compliance: Credo AI and IBM watsonx.governance; model monitoring: Fiddler AI. Effective AI governance implementation involves establishing a cross-functional committee, compiling an AI bill of materials (AI-BOM) to identify risks, and implementing policies based on frameworks like NIST AI RMF.